Method and apparatus for adjusting point cloud data acquisition trajectory, and computer readable medium

ABSTRACT

Embodiments of the present disclosure can include a method and apparatus for adjusting a point cloud data acquisition trajectory, and a computer readable medium. According to the embodiments of the present disclosure, a trajectory may be adjusted based on a characteristic extracted from point cloud data. A parameter that needs to be adjusted may be selected according to the property of the characteristic, instead of adjusting all parameters at the same time. In addition, according to the embodiments of the present disclosure, when trajectories are fused, the sequence relationships between the trajectory points in the trajectories can be considered, which avoids that a loop cannot be closed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Application No.201711479520.5, filed on Dec. 29, 2017 and entitled “Method andApparatus for Adjusting Point Cloud Data Acquisition Trajectory, andComputer Readable Medium,” the entire disclosure of which is herebyincorporated by reference.

TECHNICAL FIELD

The embodiments of the present disclosure relate to the field ofhigh-precision map, and specifically to a method and apparatus foradjusting a point cloud data acquisition trajectory, and a computerreadable medium.

BACKGROUND

In recent years, with the widespread attention to the autonomousdriving, the high-precision map technology has been rapidly developed.In particular, for the autonomous driving of a highly autonomous drivinglevel (L3), the high-precision map is absolutely necessary. In theexisting high-precision map technology, a large amount of point clouddata needs to be acquired. The point cloud data are used for the makingof the high-precision map by performing processing such as registrationon the point cloud data. Since the amount of the point cloud data isusually large, the traditional matching on the total amount of pointcloud data needs to consume a large amount of time and storage space.

SUMMARY

In summary, the embodiments of the present disclosure relate to asolution for adjusting a point cloud data acquisition trajectory.

In a first aspect, the embodiments of the present disclosure provide amethod for adjusting a point cloud data acquisition trajectory. Themethod includes: acquiring, in response to first point cloud data andsecond point cloud data acquired by a mobile entity having matchingcharacteristics, a first trajectory used by the mobile entity to acquirethe first point cloud data and a second trajectory used by the mobileentity to acquire the second point cloud data; determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory based on the matching characteristics, the first trajectory,and the second trajectory; and fusing the first trajectory and thesecond trajectory by adjusting the parameter set of the trajectorypoints.

In a second aspect, the embodiments of the present disclosure provide anapparatus for adjusting a point cloud data acquisition trajectory. Theapparatus includes: an acquiring module, configured to acquire, inresponse to first point cloud data and second point cloud data acquiredby a mobile entity having matching characteristics, a first trajectoryused by the mobile entity to acquire the first point cloud data and asecond trajectory used by the mobile entity to acquire the second pointcloud data; a determining module, configured to determine ato-be-adjusted parameter set of trajectory points in the firsttrajectory based on the matching characteristics, the first trajectory,and the second trajectory; and a fusing module, configured to fuse thefirst trajectory and the second trajectory by adjusting the parameterset of the trajectory points.

In a third aspect, the embodiments of the present disclosure provide adevice. The device includes one or more processors; and a storageapparatus, configured to store one or more programs. The one or moreprograms, when executed by the one or more processors, cause the one ormore processors to implement the method according to the first aspect ofthe present disclosure.

In a fourth aspect, the embodiments of the present disclosure provide acomputer readable medium storing a computer program. The program, whenexecuted by a processor, implements the method according to the firstaspect of the present disclosure.

It should be understood that the contents described in the presentdisclosure are not intended to limit crucial or essential features ofthe embodiments of the present disclosure, and not used to limit thescope of the present disclosure. Other features of the presentdisclosure will be easily understood through the following descriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features, advantages, and aspects of the embodimentsof the present disclosure will become more apparent in combination withthe accompanying drawings and with reference to the following detaileddescriptions. In the accompanying drawings:

FIG. 1 illustrates a block diagram of an environment in whichembodiments of the present disclosure may be implemented;

FIG. 2 illustrates a schematic diagram of trajectory fusion according tosome embodiments of the present disclosure;

FIG. 3 illustrates a flowchart of a method according to some embodimentsof the present disclosure;

FIG. 4 illustrates a flowchart of a method according to some embodimentsof the present disclosure;

FIG. 5 illustrates a schematic block diagram of an apparatus accordingto some embodiments of the present disclosure; and

FIG. 6 illustrates a block diagram of a computing device implementing aplurality of embodiments of the present disclosure.

In all the accompanying drawings, the same or similar reference numeralsrepresent the same or similar elements.

DETAILED DESCRIPTION OF EMBODIMENTS

The embodiments of the present disclosure will be described in moredetail below with reference to the accompanying drawings. Certainembodiments of the present disclosure are shown in the accompanyingdrawings. However, it should be appreciated that the present disclosuremay be implemented in various forms, and should not be interpreted asbeing limited by the embodiments described herein. Conversely, theembodiments are provided for a more thorough and complete understandingfor the present disclosure. It should be understood that theaccompanying drawings and embodiments in the present disclosure are onlyillustrative, and not used to limit the scope of protection of thepresent disclosure.

As used herein, the term “comprising” and variants thereof areopen-ended (i.e., “including, but not limited to”). The term “based on”refers to “at least partially based on.” The term “one embodiment”represents “at least one embodiment.” The terms “another embodiment”represents “at least one additional embodiment.” Definitions of otherterms will be given in the following description.

The term “point cloud data” used herein refers to a set of vectors in athree-dimensional coordinate system. These vectors are usuallyrepresented in the form of three-dimensional coordinates of X, Y, and Z,and generally used to represent the shape of the outer surface of anobject. The point cloud data may also represent the RGB color, thegray-scale value, the depth, and the partitioning result of a point. Asused herein, the term “characteristic/characteristic point” refers tothe characteristic that can reflect the essence of the image, and canidentify the target object in the image.

As mentioned above, in the traditional solutions the registration isgenerally performed directly on the point cloud data. In such case, thepoint cloud data itself needs to give a good initial value. Further,since a plurality of iterations may be required, a large amount of timeand may be consumed when directly processing the point cloud data. Thedirectly processing on the point cloud data is only suitable for thescenario with a small amount of data. However, when making thehigh-precision map, a large amount of point cloud data needs to beprocessed. Thus, the traditional solution of directly processing thepoint cloud data is not suitable for the high-precision map. Moreover,when performing the registration on the point cloud data, in general,the traditional solution is only limited to the matching relationshipbetween two frames of data without considering a point cloud dataacquisition trajectory as a whole. In such case, the trajectory such asa loop is usually caused, and thus, the closed loop cannot be accuratelyformed.

According to the embodiments of the present disclosure, a solution foradjusting a point cloud data acquisition trajectory is provided. In thissolution, the characteristics in the point cloud data are extracted forfusing the trajectories that are used to acquire the point cloud data.When the trajectories for acquiring the point cloud data are fused, thepositional parameters of the trajectory points in the trajectories maybe correspondingly adjusted based on the characteristics. Further, whenfusing the trajectories for acquiring the point cloud data, the sequencerelationships between the trajectory points in the trajectories may alsobe considered to improve the precision of the fusion.

FIG. 1 illustrates a schematic diagram of an environment 100 in whichembodiments of the present disclosure may be implemented. Theenvironment 100 may include a mobile entity 110 and a computing device106. The mobile entity may include a mobile device 101, a acquisitiondevice 102 for acquiring the point cloud data 122, and a storageapparatus 104 for storing the point cloud data 122. The mobile device101 may be any movable device. For example, the mobile device 101 may bea vehicle.

The acquisition device 102 may be disposed on the mobile device 101. Theacquisition device 102 may be any proper 3D scanning device. Forexample, the acquisition device 102 may be a lidar device.Alternatively, the acquisition device 102 may be a stereo camera. Theacquisition device 102 may also be a time-of-flight camera.

The trajectory 130 and the trajectory 140 are the trajectories of themobile device 101 at different time phases. The mobile entity 110 mayrespectively acquire point cloud data in the trajectory 130 and thetrajectory 140 through the acquisition device 102. The point cloud dataacquired by the acquisition device 102 may be stored in the storageapparatus 104. The storage apparatus 104 may be a local storageapparatus. For example, the storage apparatus 104 may be located on themobile device 101 together with the acquisition device 102. The storageapparatus 104 may also be a remote storage apparatus. For example, theacquisition device 102 may send the acquired point cloud data to theremote storage apparatus 104.

In the environment 100, a cylindrical object 1510, a planar object 1520,and a ground object 1530 are also included. Characteristics reflectingthe objects may be extracted from the point cloud data 122 acquired bythe acquisition device 102. The computing device 106 may obtain thepoint cloud data 122 from the storage apparatus 104. The computingdevice 106 may extract the characteristics from the point cloud data122, and adjust the trajectory points in the trajectory 130 and thetrajectory 140 based on the characteristics, to fuse the trajectory 130and the trajectory 140.

It should be appreciated by those skilled in the art that though FIG. 1illustrates the environment 100 in which the embodiments of the presentdisclosure may be implemented, the embodiments of the present disclosuremay be implemented in any other proper environment. The environment 100shown in FIG. 1 is merely and not restrictive. It may be appreciatedthat the numbers of the mobile entity 110, the computing device 106, thetrajectories 130 and 140, the cylindrical object 1510, the planar object1520, and the ground object 1530 shown in the environment 100 in FIG. 1are merely exemplary. The environment 100 may include any suitablenumber of the mobile entities 110, the computing devices 106, thetrajectories 130 and 140, the cylindrical objects 1510, the planarobjects 1520, and the ground objects 1530. The environment 100 may alsoinclude other components not shown.

FIG. 2 illustrates a schematic diagram of trajectory fusion 200according to some embodiments of the present disclosure. The trajectory210 (which may be referred to as a “first trajectory”) and thetrajectory 220(which may be referred to as a “second trajectory”) may bethe trajectories of the mobile entity 110 acquiring the point cloud dataat different time phases. The trajectory 210 includes a plurality oftrajectory points, for example, the trajectory points 2110-1, 2110-2,2110-3, 2110-4, 2110-5, 2110-6, 2110-7, 2110-8, . . . , 2110-N (notshown) (collectively referred to as the “trajectory points 2110”).Similarly, the trajectory 220 includes a plurality of trajectory points,for example, the trajectory points 2210-1, 2210-2, 2210-3, 2210-4,2210-5, 2210-6, 2210-7, 2210-8, . . . , 2210-N (not shown) (collectivelyreferred to as the “trajectory points 2210”).

The trajectory point has corresponding positional parameters, which maybe obtained in various ways. For example, the positional parameters ofthe trajectory point may be obtained by a global positioning system(GPS). The positional parameters of the trajectory point may includeparameters of the X-axis, the Y-axis, and the Z-axis in a Cartesiancoordinate system. The positional parameters of the trajectory point mayalso include angle parameters. For example, the positional parametersmay include a parameter of a pitch angle rotating around the X-axis, aparameter of a yaw angle rotating around the Y-axis, and a parameter ofa roll angle rotating around the Z-axis.

The characteristic 230-1 and the characteristic 230-2 (collectivelyreferred to as the “characteristics 230”) are characteristics which canbe extracted both from the point cloud data acquired based on thetrajectory 210 and from the point cloud data acquired based on thetrajectory 220. The characteristic 230-1 and the characteristic 230-2may be cylindrical object characteristics representing a cylindricalobject (e.g., the cylindrical object 1510). Alternatively, thecharacteristic 230-1 and the characteristic 230-2 may be groundcharacteristics representing a ground object (e.g., the ground object1530). The characteristic 230-1 and the characteristic 230-2 may also beplanar characteristics representing a planar object (e.g., the planarobject 1520). It may be appreciated that the characteristic 230-1 andthe characteristic 230-2 may be different characteristics, and may alsobe the same characteristic. The embodiments according to the presentdisclosure will be further described below with reference to FIGS. 3-4.It may be understood that the example in which two trajectories areadjusted is described only for the purpose of explanation. In theembodiments of the present disclosure, a plurality of trajectories maybe adjusted, for example, three or more trajectories may be adjusted.

At block 310, if the first point cloud data and the second point clouddata acquired by the mobile entity 110 have the matching characteristics230, the computing device 106 acquires the trajectory 210 used by themobile entity 110 to acquire the first point cloud data and thetrajectory 220 used by the mobile entity 110 to acquire the second pointcloud data. The computing device 106 may obtain the characteristics ofthe point cloud data in any suitable way. For example, the computingdevice 106 may obtain the characteristics in the point cloud datathrough a manual input. In some embodiments, a plurality of sets ofpoint cloud data acquired by the mobile entity 110 all have matchingcharacteristics, and the computing device 106 may obtain thetrajectories used by the mobile entity 110 to acquire the sets of pointcloud data. For example, the computing device 106 may obtain thetrajectories 210, 220, and 240 (not shown), which are used by the mobileentity 110 to acquire these sets of point cloud data.

Alternatively, in some embodiments, the computing device 106 may extractthe characteristic in each frame of point cloud data. Specifically, thecomputing device 106 may determine a ground point cloud, a planar pointcloud, the point cloud of the cylindrical object, and/or other pointclouds from the point cloud data. Then, the computing device 106 mayrespectively extract the characteristics in the ground point cloud, theplanar point cloud, and the point cloud of the cylindrical object. Forexample, the computing device 106 may extract the ground characteristicbased on the ground point cloud, the planar characteristic based on theplanar point cloud, the cylindrical object characteristic based on thepoint cloud of the cylindrical object, and so on.

In the embodiments, the device 106 may convert the characteristicsextracted from different pieces of point cloud data into the samecoordinate system. If the difference between the characteristicsextracted from the different pieces of point cloud data in the samecoordinate system is within a predetermined threshold (also referred toas “threshold difference”), the characteristics may be considered to bematching. That is, the different pieces of point cloud data from whichthe characteristics are extracted have matching characteristics. Forexample, when the characteristic 230-1′ (not shown) extracted from thefirst point cloud data and the characteristic 230-1″ (not shown)extracted from the second point cloud data are matching, the first pointcloud data and the second point cloud data have the matchingcharacteristics 230-1. The characteristic (e.g., 230-1′) is rigidlyconnected to the trajectory point to which the characteristic belongs.On this basis, the computing device 106 may obtain the trajectory 210for acquiring the first point cloud data and the trajectory 220 foracquiring the second point cloud data. In this way, the computing device106 may greatly reduce the amount of the point cloud data that needs tobe processed, thereby enhancing the processing speed.

At block 320, the computing device 106 determines a to-be-adjustedparameter set of the trajectory points 2110 in the trajectory 210 basedon the matching characteristics 230, the trajectory 210 and thetrajectory 220. For example, the characteristic 230-1 is a matchingcharacteristic, and the trajectory points 2110-1, 2110-2, and 2110-3 inthe trajectory 210 and the trajectory points 2210-1, 2210-2, and 2210-3in the trajectory 220 are the trajectory points associated with thecharacteristic. The computing device 106 may determine a set ofparameters having maximum differences between the trajectory points2110-1, 2110-2, and 2110-3 and the trajectory points 2210-1, 2210-2, and2210-3. The computing device 106 may define the set of parameters havingthe maximum differences as the to-be-adjusted parameter set of thetrajectory points. For example, the computing device 106 may define theZ-axis parameter, the pitch angle parameter and the roll angle parameterhaving the maximum differences as the to-be-adjusted parameter set. Inthis way, a parameter may be correspondingly adjusted according to theproperties of the characteristics, instead of adjusting all theparameters at the same time, thereby further improving the efficiency ofthe fusion. As described above, in some embodiments, the computingdevice 106 may acquire a plurality of trajectories of the mobile entity110 acquiring the point cloud data. The computing device 106 maydetermine a to-be-adjusted parameter set of trajectory points in thetrajectories based on the matching characteristics and thesetrajectories.

The methods that may be implemented at block 320 will be described belowwith reference to FIG. 4. It may be appreciated that the method 400illustrated in FIG. 4 is merely illustrative, and not restrictive. Atblock 410, the computing device 106 may search for the matchingcharacteristics with a large search radius (which may be referred to asa “first search radius”). For example, the computing device 106 maysearch for the characteristic that may be matching (e.g., thecharacteristic 230-1″ (not shown) extracted from the point cloud dataacquired along the trajectory 220) in space within a certain distanceand with the characteristic 230-1′ (not shown) extracted from the pointcloud data acquired along the trajectory 210 as a center.

At block 420, if the matching characteristics 230-1 are the groundcharacteristics, the device 106 may determine that the to-be-adjustedparameter set of trajectory points is a ground parameter set. In someembodiments, the ground parameter set includes a Z-axis parameter in theCartesian coordinate system. Alternatively/additionally, the groundparameter set further includes a parameter of a pitch angle rotatingaround the X-axis, and a parameter of a roll angle rotating around theZ-axis. As merely an example, the device 106 may adjust the Z-axisparameters, the pitch angle parameters, and the roll angle parameters ofthe trajectory points in the trajectory 210 and the trajectory 220 to bethe same. For example, the device 106 may adjust the heights of thetrajectory 210 and the trajectory 220 in the Z direction, so that thetwo trajectories coincide in the Z-direction, and the groundcharacteristics are the same as the normal vectors of the groundsrespectively corresponding to the trajectory 210 and the trajectory 220.

At block 430, the computing device 106 may search for the matchingcharacteristics with a small search radius (which may be referred to asa “second search radius”). The computing device 106 has searched thecharacteristics with the large radius at block 410 to perform theadjusting. The small search radius at block 430 may make theto-be-adjusted parameters of the trajectory 210 and the trajectory 220more precise, thereby obtaining a more accurate fusion result.

At block 440, if the matching characteristics 230-2 are the cylindricalobject characteristics, the device 106 may determine that theto-be-adjusted parameter set of trajectory points is a cylindricalobject parameter set. In some embodiments, the cylindrical objectparameter set may include X-axis parameters and Y-axis parameters in theCartesian coordinate system. For example, in an actual applicationscenario, the cylindrical object may be an object such as a street lamp.As merely an example, the device 106 may adjust the X-axis parametersand the Y-axis parameters of the trajectory points in the trajectory 210and the trajectory 220 to be the same.

At block 450, the computing device 106 may search for the matchingcharacteristics with a smaller search radius (which may be referred toas a “third search radius”). As described above, the computing device106 has performed two searches for the matching characteristics. Thus,the matching degree of the matching characteristics searched at block450 is higher.

At block 460, if the matching characteristics are planarcharacteristics, the device 106 may determine that the to-be-adjustedparameter set of trajectory points is a planar parameter set. In someembodiments, the planar parameter set may include X-axis parameters andY-axis parameters in the Cartesian coordinate system.Alternatively/additionally, the planar parameter set may further includea yaw angle rotating around the Y axis. In the actual scenario, theplanar characteristic may be a small plane (e.g., a billboard)perpendicular to the ground surface. As merely an example, the device106 may adjust the X-axis parameters and the Y-axis parameters of thetrajectory points in the trajectory 210 and the trajectory 220 and theyaw angle rotating around the Y axis to be the same, which makes thedistance between the planes corresponding to the planar characteristicsand respectively associated with the trajectory 210 and the trajectory220 is 0 in the normal vector.

It may be appreciated that the sequence of adjusting the parameter setsis merely exemplary, and the parameter sets may be adjusted in anysuitable sequence. The sequence in which the parameter sets are adjustedis associated with the characteristics of the point cloud data. In thisway, the embodiments of the present disclosure may adjust the searchrange for the matching characteristics and the sequence of theto-be-adjusted parameter sets based on the characteristics of the pointcloud data, thereby achieving a better optimization sequence.

Returning to FIG. 3, at block 330, the computing device 106 may fuse thetrajectory 210 and the trajectory 220 by adjusting the parameter set ofthe trajectory points 2110. In some embodiments, the computing device106 may fuse the trajectory 210 and the trajectory 220 based on thesequence relationship between the trajectory points in the trajectory210 and the sequence relationship between the trajectory points in thetrajectory 220. The device 106 may optimize the trajectory 210 and thetrajectory 220 in any suitable way, to achieve a better fusion of thetrajectory 210 and the trajectory 220. In some embodiments, the sequencerelationship between the trajectory points in the trajectory 210 and thesequence relationship between the trajectory points in the trajectory220 may be used as the parameters for optimizing the trajectory 210 andthe trajectory 220. In this way, in the embodiments of the presentdisclosure the trajectories are fused using the positions of thetrajectory points as a reference, which effectively avoids the problemthat the loop cannot be closed, and further improves the accuracy of thetrajectory fusion. As described above, in some embodiments, thecomputing device 106 may acquire the plurality of trajectories of themobile entity 110 acquiring the point cloud data. The computing device106 may fuse the trajectories by adjusting the parameter sets of thetrajectory points in the trajectories. That is, the computing device 106may fuse the plurality of trajectories simultaneously.

FIG. 5 illustrates a schematic block diagram of an apparatus 500 foradjusting a point cloud data acquisition trajectory according to theembodiments of the present disclosure. As shown in FIG. 5, the apparatus500 includes: an acquiring module 510, configured to acquire, inresponse to first point cloud data and second point cloud data acquiredby a mobile entity having matching characteristics, a first trajectoryused by the mobile entity to acquire the first point cloud data and asecond trajectory used by the mobile entity to acquire the second pointcloud data; a determining module 530, configured to determine ato-be-adjusted parameter set of trajectory points in the firsttrajectory based on the matching characteristics, the first trajectory,and the second trajectory; and a fusing module 550, configured to fusethe first trajectory and the second trajectory by adjusting theparameter set of the trajectory points.

In some embodiments, the acquiring module 510 includes: a characteristicacquiring sub-module (not shown), configured to acquire a firstcharacteristic associated with the first point cloud data and a secondcharacteristic associated with the second point cloud data; and amatching characteristic determining sub-module (not shown), configuredto determine, in response to a difference between the firstcharacteristic and the second characteristic being less than a thresholddifference, the first point cloud data and the second point cloud datahaving the matching characteristics.

In some embodiments, the determining module 530 includes: a parameterset determining sub-module (not shown), configured to determine a set ofparameters of a trajectory point in the first trajectory, the trajectorypoint having a maximum difference from a corresponding trajectory pointin the second trajectory; and determine the to-be-adjusted parameter setof trajectory points based on the set of parameters of the trajectorypoint.

In some embodiments, the determining module 530 includes: a groundparameter set determining sub-module (not shown), configured todetermine, in response to the matching characteristics being groundcharacteristics, the to-be-adjusted parameter set of trajectory pointsto be a ground parameter set, wherein the ground parameter set includesat least one of: a Z-axis parameter, a parameter of a pitch anglerotating around an X-axis, or a parameter of a roll angle rotatingaround a Z-axis in a Cartesian coordinate system.

In some embodiments, the determining module 530 includes: a cylindricalobject parameter set determining sub-module, configured to determine, inresponse to the matching characteristics being cylindrical objectcharacteristics, the to-be-adjusted parameter set of trajectory pointsto be a cylindrical object parameter set, wherein the cylindrical objectparameter set includes at least one of: an X-axis parameter, or a Y-axisparameter in the Cartesian coordinate system.

In some embodiments, the determining module 530 includes: a planarparameter set determining sub-module (not shown), configured todetermine, in response to the matching characteristics being planarcharacteristics, the to-be-adjusted parameter set of trajectory pointsto be a planar parameter set, wherein the planar parameter set includesat least one of: the X-axis parameter, the Y-axis parameter, or aparameter of a yaw angle rotating around a Y-axis in the Cartesiancoordinate system.

In some embodiments, the fusing module 550 includes: a fusing sub-module(not shown), configured to fuse the first trajectory and the secondtrajectory based on a sequence relationship between trajectory points inthe first trajectory and a sequence relationship between trajectorypoints in the second trajectory.

FIG. 6 shows a schematic block diagram of a device 600 capable ofimplementing various embodiments of the present disclosure. The device600 may be used to implement the computing device 106 in FIG. 1. Asshown in the figure, the device 600 includes a central processing unit(CPU) 601 that may perform various appropriate actions and processing inaccordance with computer program instructions stored in a read onlymemory (ROM) 602 or computer program instructions loaded into a randomaccess memory (RAM) 603 from a storage unit 608. In the RAM 603, variousprograms and data required for the operation of the device 600 may alsobe stored. The CPU 601, the ROM 602, and the RAM 603 are connected toeach other through a bus 604. An input/output (I/O) interface 605 isalso coupled to the bus 604.

A plurality of components in the device 600 are coupled to the I/Ointerface 605, including: an input unit 606, such as a keyboard or amouse; an output unit 607, such as various types of displays, orspeakers; the storage unit 608, such as a disk or an optical disk; and acommunication unit 609 such as a network card, a modem, or a wirelesscommunication transceiver. The communication unit 609 allows the device600 to exchange information/data with other devices over a computernetwork such as the Internet and/or various telecommunication networks.

The processing unit 601 performs the various methods and processesdescribed above, such as the process 300 and/or the process 400. Forexample, in some embodiments, the process 300 and/or the process 400 maybe implemented as a computer software program that is tangibly embodiedin a machine readable medium, such as the storage unit 608. In someembodiments, some or all of the computer programs may be loaded and/orinstalled onto the device 600 via the ROM 602 and/or the communicationunit 609. When a computer program is loaded into the RAM 603 andexecuted by the CPU 601, one or more of the actions or steps of theprocess 300 and/or the process 400 described above may be performed.Alternatively, in other embodiments, the CPU 601 may be configured toperform the process 300 and/or the process 400 by any other suitablemeans (e.g., by means of firmware).

The functions described herein above may be performed, at least in part,by one or more hardware logic components. For example, and withoutlimitation, exemplary types of hardware logic components that may beused include: Field Programmable Gate Array (FPGA), Application SpecificIntegrated Circuit (ASIC), Application Specific Standard Product (ASSP),System on Chip (SOC), Complex Programmable Logic Device (CPLD), and thelike.

In general, the various embodiments of the present disclosure may beimplemented in hardware, a special circuit, special software, a speciallogic, or any combination thereof. Some aspects may be implemented inthe hardware, while other aspects may be implemented in firmware orsoftware which may be executed by a controller, a microprocessor orother computing devices. When the various aspects of the embodiments ofthe present disclosure are illustrated or described as block diagrams,flowcharts, or some other diagrams for explanation, it will beunderstood that the blocks, devices, systems, techniques, or methodsdescribed herein may be used as non-limiting examples that areimplemented in the hardware, the software, the firmware, the specialcircuit or logic, the general purpose hardware or controller, othercomputing devices, or some combinations thereof.

As an example, the embodiments of the present disclosure may bedescribed in the context of machine-executable instructions. Themachine-executable instructions are included in, for example, programmodules executed in a device on a real or virtual processor of a target.Generally, the program modules include routines, programs, libraries,objects, classes, components, data structures, and the like, whichperform a particular task or implement a particular abstract datastructure. In various embodiments, the functions of the program modulesmay be combined or divided among the described program modules. Themachine-executable instructions for the program modules may be executedin a local or distributed device. In the distributed device, the programmodules may be in a local storage medium and a remote storage medium.

Program codes for implementing the method of some embodiments of thepresent disclosure may be written in any combination of one or moreprogramming languages. These program codes may be provided to aprocessor or controller of a general purpose computer, special purposecomputer or other programmable data processing apparatus such that theprogram codes, when executed by the processor or controller, enables thefunctions/operations specified in the flowcharts and/or block diagramsbeing implemented. The program codes may execute entirely on themachine, partly on the machine, as a stand-alone software package partlyon the machine and partly on the remote machine, or entirely on theremote machine or server.

In the context of some embodiments of the present disclosure, themachine readable medium may be a tangible medium that may contain orstore programs for use by or in connection with an instruction executionsystem, apparatus, or device. The machine readable medium may be amachine readable signal medium or a machine readable storage medium. Themachine readable medium may include, but is not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples of the machine readable storagemedium may include an electrical connection based on one or more wires,portable computer disk, hard disk, random access memory (RAM), read onlymemory (ROM), erasable programmable read only memory (EPROM or flashmemory), optical storage device, magnetic storage device, or anysuitable combination of the foregoing.

In addition, although operations are described in a specific order, thisshould not be understood that such operations are required to beperformed in the specific order shown or in sequential order, or allillustrated operations should be performed to achieve the desiredresult. Multitasking and parallel processing may be advantageous incertain circumstances. Likewise, although several specificimplementation details are included in the above discussion, theseshould not be construed as limiting the scope of the present disclosure.Certain features described in the context of separate embodiments mayalso be implemented in combination in a single implementation.Conversely, various features described in the context of a singleimplementation may also be implemented in a plurality ofimplementations, either individually or in any suitable sub-combination.

Although the embodiments of the present disclosure are described inlanguage specific to structural features and/or method logic actions, itshould be understood that the subject matter defined in the appendedclaims is not limited to the specific features or actions describedabove. Instead, the specific features and actions described above aremerely exemplary forms of implementing the claims.

What is claimed is:
 1. A method for adjusting a point cloud dataacquisition trajectory, comprising: acquiring, in response to firstpoint cloud data and second point cloud data acquired by a mobile entityincluding matching characteristics, a first trajectory used by themobile entity to acquire the first point cloud data and a secondtrajectory used by the mobile entity to acquire the second point clouddata; determining a to-be-adjusted parameter set of trajectory points inthe first trajectory based on the matching characteristics, the firsttrajectory, and the second trajectory; and fusing the first trajectoryand the second trajectory by adjusting the parameter set of thetrajectory points, wherein the method is performed by at least oneprocessor.
 2. The method according to claim 1, wherein the determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory comprises: determining a set of parameters of a trajectorypoint in the first trajectory, the trajectory point including a maximumdifference from a corresponding trajectory point in the secondtrajectory; and determining the to-be-adjusted parameter set oftrajectory points based on the set of parameters of the trajectorypoint.
 3. The method according to claim 1, wherein the determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory comprises: determining, in response to the matchingcharacteristics being ground characteristics, the to-be-adjustedparameter set of trajectory points to be a ground parameter set, whereinthe ground parameter set includes at least one of: a Z-axis parameter, aparameter of a pitch angle rotating around an X-axis, or a parameter ofa roll angle rotating around a Z-axis in a Cartesian coordinate system.4. The method according to claim 1, wherein the determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory comprises: determining, in response to the matchingcharacteristics being cylindrical object characteristics, theto-be-adjusted parameter set of trajectory points to be a cylindricalobject parameter set, wherein the cylindrical object parameter setincludes at least one of: an X-axis parameter, or a Y-axis parameter ina Cartesian coordinate system.
 5. The method according to claim 1,wherein the determining a to-be-adjusted parameter set of trajectorypoints in the first trajectory comprises: determining, in response tothe matching characteristics being planar characteristics, theto-be-adjusted parameter set of trajectory points to be a planarparameter set, wherein the planar parameter set includes at least oneof: an X-axis parameter, an Y-axis parameter, or a parameter of a yawangle rotating around a Y-axis in a Cartesian coordinate system.
 6. Themethod according to claim 1, wherein the fusing the first trajectory andthe second trajectory comprises: fusing the first trajectory and thesecond trajectory based on a sequence relationship between trajectorypoints in the first trajectory and a sequence relationship betweentrajectory points in the second trajectory.
 7. The method according toclaim 1, wherein the acquiring the first trajectory and the secondtrajectory comprises: acquiring a first characteristic associated withthe first point cloud data and a second characteristic associated withthe second point cloud data; and determining, in response to adifference between the first characteristic and the secondcharacteristic being less than a threshold difference, the first pointcloud data and the second point cloud data including the matchingcharacteristics.
 8. An apparatus for adjusting a point cloud dataacquisition trajectory, comprising: at least one processor; and a memorystoring instructions, the instructions when executed by the at least oneprocessor, cause the at least one processor to perform operations, theoperations comprising: acquiring, in response to first point cloud dataand second point cloud data acquired by a mobile entity includingmatching characteristics, a first trajectory used by the mobile entityto acquire the first point cloud data and a second trajectory used bythe mobile entity to acquire the second point cloud data; determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory based on the matching characteristics, the first trajectory,and the second trajectory; and fusing the first trajectory and thesecond trajectory by adjusting the parameter set of the trajectorypoints.
 9. The apparatus according to claim 8, wherein the determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory comprises: determining a set of parameters of a trajectorypoint in the first trajectory, the trajectory point including a maximumdifference from a corresponding trajectory point in the secondtrajectory; and determining the to-be-adjusted parameter set oftrajectory points based on the set of parameters of the trajectorypoint.
 10. The apparatus according to claim 8, wherein determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory comprises: determining, in response to the matchingcharacteristics being ground characteristics, the to-be-adjustedparameter set of trajectory points to be a ground parameter set, whereinthe ground parameter set includes at least one of: a Z-axis parameter, aparameter of a pitch angle rotating around an X-axis, or a parameter ofa roll angle rotating around a Z-axis in a Cartesian coordinate system.11. The apparatus according to claim 8, wherein determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory comprises: determining, in response to the matchingcharacteristics being cylindrical object characteristics, theto-be-adjusted parameter set of trajectory points to be a cylindricalobject parameter set, wherein the cylindrical object parameter setincludes at least one of: an X-axis parameter, or a Y-axis parameter ina Cartesian coordinate system.
 12. The apparatus according to claim 8,wherein determining a to-be-adjusted parameter set of trajectory pointsin the first trajectory comprises: determining, in response to thematching characteristics being planar characteristics, theto-be-adjusted parameter set of trajectory points to be a planarparameter set, wherein the planar parameter set includes at least oneof: an X-axis parameter, an Y-axis parameter, or a parameter of a yawangle rotating around a Y-axis in a Cartesian coordinate system.
 13. Theapparatus according to claim 8, wherein the fusing the first trajectoryand the second trajectory comprises: fusing the first trajectory and thesecond trajectory based on a sequence relationship between trajectorypoints in the first trajectory and a sequence relationship betweentrajectory points in the second trajectory.
 14. The apparatus accordingto claim 8, wherein the acquiring the first trajectory and the secondtrajectory comprises: acquiring a first characteristic associated withthe first point cloud data and a second characteristic associated withthe second point cloud data; and determining, in response to adifference between the first characteristic and the secondcharacteristic being less than a threshold difference, the first pointcloud data and the second point cloud data including the matchingcharacteristics.
 15. A non-transitory computer storage medium storing acomputer program, the computer program when executed by one or moreprocessors, causes the one or more processors to perform operations, theoperations comprising: acquiring, in response to first point cloud dataand second point cloud data acquired by a mobile entity includingmatching characteristics, a first trajectory used by the mobile entityto acquire the first point cloud data and a second trajectory used bythe mobile entity to acquire the second point cloud data; determining ato-be-adjusted parameter set of trajectory points in the firsttrajectory based on the matching characteristics, the first trajectory,and the second trajectory; and fusing the first trajectory and thesecond trajectory by adjusting the parameter set of the trajectorypoints.